1. Satellite SST Radiance Assimilation and SST Data Impacts James Cummings Naval Research Laboratory Monterey, CA 93943 2013 Sea Surface Temperature Science Team Meeting Seattle, Washington, USA Global HYCOM SST 25 Oct 2013 00Z

2. incorporate impact of real atmosphere above SST field remove atmospheric signals in SST radiance data include variables known to affect satellite SST radiances: atmospheric temperature atmospheric water vapor SST aerosols (dust, smoke) - not used yet all variables are available from NWP, aerosol, and ocean model forecasting systems SST Radiance Assimilation: Objectives GOES-15 Water Vapor HYCOM SST Forecast

3. SST Radiances (BTs) Calibration, QC, Cloud Clearing Compute Differences: δ BTs 3DVAR Minimization SST Inverse Model: δ SST NWP Fields CRTM Jacobians (  T b /  x) CRTM Forward Model: TOA-BTs SST Radiance Assimilation: Observation Operator Other Observations: T,S,U,V Observation & First Guess Errors Ocean Model SST SST Radiance Monitoring

4. GOES METOP GAC METOP LAC NOAA GAC NOAA LAC NPP VIIRS SST Radiance Assimilation: Observing Systems NAVOCEANO SST Radiance Data: GOES-13, GOES-15 METOP-A, METOP-B (GAC and LAC) NOAA-18, NOAA-19 (GAC and LAC) NPP VIIRS COMS-1 No radiance data available for MSG and MTSAT

5. Ch3 Ch4 Ch5 NAVO Buoy Matchup Data 25 Aug to 8 Sep 2013 NWP Priors: 90 min, 16 km METOP-A CRTM Forward Model: Obs vs. Simulated BTs NPP VIIRS NOAA-19

6. CRTM Forward Model: Bias Correction by definition satellite SST radiances are cloud free but NWP priors may have clouds TCWV bias correction modeled as quadratic function (red curves) calculated for all satellites and all channels using 15-day sliding time window of NAVO SST radiances and buoy matchups METOP-A NOAA-19 Liquid Water Path kg/m 2 Liquid Water Path kg/m 2

7. Computes SST correction (  T sst ) given TOA BT innovations (  BT) and CRTM Jacobians (J): Requires specification of prior error statistics: air temperature: ε t specific humidity: ε q sea surface temperature: ε sst satellite BTs + radiometric error: ε bt Partitions  BT innovations into  T sst,  T a,  Q a corrections SST Inverse Model

8. NAVGEM Ensemble: Specific Humidity SST Inverse Model: NWP Prior Errors Provides situation dependent uncertainty of atmospheric forecasts Specific humidity variability greatest at low latitudes

9. NAVGEM Ensemble: Air Temperature SST Inverse Model: NWP Prior Errors Air temperature variability greatest at high latitudes

10. SST Inverse Model: Ocean SST Errors Atlantic Indian Pacific Computed from time history of model forecast differences at update cycle interval (24-hr) SST variability greatest in tropics, Antarctic circumpolar, and western boundary currents HYCOM 3DVAR SST Background Error 5 Sep 2013

11. SST Inverse Model: NAVO SST Corrections METOP-A 5 Sep 2013 Day Night large positive SST corrections at high latitude dry atmospheres shows globally defined NAVO SST retrievals biased in some regimes some day/night differences in SST corrections (e.g., U.S. west coast)  T sst

12. SST Inverse Model: Air Temperature Corrections METOP-A 5 Sep 2013 Day Night air temp corrections generally small given expected range of atmospheric temperatures corrections tend to be largest at high latitudes where NWP model air temperature variability is high (some exceptions, e.g. WestPAC) TaTa

13. SST Inverse Model: Water Vapor Corrections METOP-A 5 Sep 2013 water vapor corrections tend to be greater at low latitudes where NWP model water vapor variability is high Day Night QaQa

14. SST Inverse Model: Correction Data Density METOP-A METOP-B NOAA-18 NOAA-19 NAVO SST Correction vs. Total Column Water Vapor: 5 Sep 2013  T sst

15. Observation (y) NCODA 3DVAR HYCOM Forecast (x f ) Forecast Error J: (  J/  x f ) Background (x b ) Analysis (x a ) Adjoint of HYCOM Observation Sensitivity (  J/  y) Initial Condition Sensitivity (  J/  x a ) Adjoint of 3DVAR What is the impact of observations on measures of forecast error (J) ? Data Impact System Analysis – Forecast System Observation Impact Equation (Langland and Baker, 2004)

16. Observation Impact Equation: Interpretation < 0.0 the observation is BENEFICIAL - forecast errors decrease For any observation assimilated, if... > 0.0 the observation is NON-BENEFICIAL - forecast errors increase Non-beneficial impacts: - not expected, assimilation should decrease forecast error - if it is persistent, may indicate observing system problems

17. SST Data Impact: Satellite Observing Systems Global HYCOM - November 2012 Per Ob Data Impacts for Reducing HYCOM 48-hr SST Forecast Error Atlantic Pacific Data assimilated are NAVOCEANO SST retrievals

18. SST Data Impact: Non-beneficial Impacts METOP-A - November 2012 (averaged at model grid locations) Pacific Atlantic

19. SST Data Impact: Non-beneficial Impacts NOAA-19 - November 2012 (averaged at model grid locations) Pacific Atlantic

20. SST Data Impact: Non-beneficial Impacts METOP-A vs. NOAA-19 - November 2012 (averaged at model grid locations) METOP-A NOAA-19 More non-beneficial impacts from assimilation of NOAA-19 retrievals than METOP-A Differences in quality of AVHRR instruments on the two satellites?

21. SST Data Impact: Non-beneficial Impacts GOES - November 2012 (averaged at model grid locations) Pacific Atlantic

22. CRTM forward and SST inverse modeling: removes atmospheric signals from radiance observations real atmosphere needed to understand changes in TOA BTs Data Impacts: assimilation of satellite SST data reduces HYCOM forecast error non-beneficial impacts show geographic, instrument, and satellite zenith angle dependencies Science Team Objectives: data classification: TOA-BT  T sst,  T a,  Q a partitions and data impacts provide pixel level information data merging and gridding: global, model-based 3DVAR with full error analysis and data impact components communication: contribution to MICROS? Other inter-comparison activities along the lines of the ESA CCI? Assimilation and Data Impact: Conclusions

23. Questions?